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Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4
Andrei Pruteanu
Andrei Pruteanu
This course will teach you how to create deep-learning algorithms for detecting and mitigating anomalies in data such as time series.
Read more
This course will teach you how to create deep-learning algorithms for detecting and mitigating anomalies in data such as time series.
Read more
This course will teach you how to create deep-learning algorithms for detecting and mitigating anomalies in data such as time series.
In this course, Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4, you’ll learn to spot specific patterns in large datasets that can be labelled as anomalies. First, you’ll explore how to precisely define anomalies in data. Next, you’ll discover detection algorithms. Finally, you’ll learn how to mitigate anomalous data. When you’re finished with this course, you’ll have the skills and knowledge of creating machine learning algorithms needed for dealing with various anomalies in data.
Register for this course and see more details by visiting:
OpenCourser.com/course/2zvb3k/detecting
What's inside
Syllabus
Course Overview
Introduction
Exploratory Data Analysis
Definition and Anomaly Types
Read more
Syllabus
Course Overview
Introduction
Exploratory Data Analysis
Definition and Anomaly Types
Read more
Detection Algorithms
Mitigation Techniques
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores methods which are highly relevant to data science and machine learning
Strengthens an existing foundation for intermediate learners
Taught by instructors recognized for their work in machine learning
Develops professional expertise in anomaly detection
Requires learners to come in with extensive background knowledge first
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Activities
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Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4.
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Career center
Learners who complete Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists are experts at collecting, cleaning, analyzing, and presenting data. They use their skills to solve problems, predict trends, and develop new products and services. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical, statistical, and programming skills needed to be a successful data scientist. This course's focus on anomaly detection is particularly relevant, as data scientists often need to identify and address anomalies in their data.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design, build, and maintain machine learning models. They use their skills to develop solutions to a variety of problems, including fraud detection, customer segmentation, and anomaly detection. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the technical skills needed to be a successful machine learning engineer. This course's focus on deep learning and TensorFlow is particularly relevant, as these are essential technologies in the field of machine learning.
Business Analyst
Business Analyst
Business analysts help organizations improve their performance by analyzing data and identifying opportunities for improvement. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and problem-solving skills needed to be a successful business analyst. This course's focus on anomaly detection is particularly relevant, as business analysts often need to identify and address anomalies in their data.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and analytical techniques to solve problems and improve decision-making. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and problem-solving skills needed to be a successful operations research analyst. This course's focus on anomaly detection is particularly relevant, as operations research analysts often need to identify and address anomalies in their data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use their skills to solve problems, predict trends, and develop new products and services. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and statistical skills needed to be a successful statistician. This course's focus on anomaly detection is particularly relevant, as statisticians often need to identify and address anomalies in their data.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. They work with a variety of technologies, including programming languages, databases, and operating systems. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the technical skills needed to be a successful software engineer. This course's focus on deep learning and TensorFlow is particularly relevant, as these are essential technologies in the field of software engineering.
Data Analyst
Data Analyst
Data analysts discover, clean, and present data that can be used to analyze trends, monitor performance, and support decision-making. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and technical skills needed to work with data in a meaningful way. This course's exploration of exploratory data analysis and anomaly detection algorithms can be particularly valuable.
Financial Analyst
Financial Analyst
Financial analysts help organizations make investment decisions by analyzing data and providing recommendations. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and financial skills needed to be a successful financial analyst. This course's focus on anomaly detection is particularly relevant, as financial analysts often need to identify and address anomalies in their data.
Risk Analyst
Risk Analyst
Risk analysts help organizations identify and manage risks. They use their skills to assess risks, develop mitigation plans, and monitor risks. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and risk management skills needed to be a successful risk analyst. This course's focus on anomaly detection is particularly relevant, as risk analysts often need to identify and address anomalies in their data.
Data Engineer
Data Engineer
Data engineers build and maintain the infrastructure that supports data analysis. They work with a variety of technologies, including databases, data warehouses, and big data platforms. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the technical skills needed to be a successful data engineer. This course's focus on anomaly detection is particularly relevant, as data engineers often need to identify and address anomalies in their data.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical techniques to analyze data and make investment decisions. They work with a variety of financial institutions, including hedge funds, investment banks, and asset management companies. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and quantitative skills needed to be a successful quantitative analyst. This course's focus on anomaly detection is particularly relevant, as quantitative analysts often need to identify and address anomalies in their data.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work with a variety of industries, including insurance, healthcare, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the analytical and risk management skills needed to be a successful actuary. This course's focus on anomaly detection is particularly relevant, as actuaries often need to identify and address anomalies in their data.
Data Science Manager
Data Science Manager
Data science managers lead teams of data scientists and oversee the development and implementation of data science projects. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the leadership and management skills needed to be a successful data science manager. This course's focus on anomaly detection is particularly relevant, as data science managers often need to identify and address anomalies in their data.
Machine Learning Manager
Machine Learning Manager
Machine learning managers lead teams of machine learning engineers and oversee the development and implementation of machine learning projects. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the leadership and management skills needed to be a successful machine learning manager. This course's focus on anomaly detection is particularly relevant, as machine learning managers often need to identify and address anomalies in their data.
Artificial Intelligence Manager
Artificial Intelligence Manager
Artificial intelligence managers lead teams of artificial intelligence engineers and oversee the development and implementation of artificial intelligence projects. They work with a variety of industries, including healthcare, manufacturing, and finance. Courses like Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4 can be very helpful in building the leadership and management skills needed to be a successful artificial intelligence manager. This course's focus on anomaly detection is particularly relevant, as artificial intelligence managers often need to identify and address anomalies in their data.
For more career information including salaries, visit:
OpenCourser.com/course/2zvb3k/detecting
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Detecting Data Anomalies using Deep Learning Techniques with TensorFlow 2.4.
A comprehensive resource for machine learning with Scikit-Learn, Keras, and TensorFlow. Covers a wide range of machine learning algorithms and techniques, with a focus on practical applications.
A must-have reference for TensorFlow and Keras practitioners. Provides a comprehensive overview of deep learning models and architectures, along with practical guidance on building and training deep learning models.
A visually appealing and intuitive introduction to deep learning. Provides a clear and accessible explanation of the fundamental concepts and algorithms of deep learning.
A practical guide to deep learning for coders. Provides a hands-on approach to building and training deep learning models using Fastai and PyTorch.
A comprehensive textbook on time series analysis. Provides a rigorous treatment of the field, covering topics such as stationarity, seasonality, and forecasting methods.
An excellent resource for beginners in data science. Covers the fundamental concepts and techniques of data science, including data cleaning, data manipulation, and data visualization.
Save
A classic work on time series analysis and forecasting. Provides a comprehensive overview of the field, covering topics such as stationarity, seasonality, and forecasting methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2zvb3k/detecting
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Time
92 hours
Level
Advanced
Via
Pluralsight
Instructor
Andrei Pruteanu
Language
English
Good to know
Explores methods which are highly relevant to data science and machine learning
Strengthens an existing foundation for intermediate learners
Taught by instructors recognized for their work in machine learning
Develops professional expertise in anomaly detection
Requires learners to come in with extensive background knowledge first
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